Emergent Trends in Machine Learning: Business Autonomy

The general perception of machine learning is that it involves sophisticated algorithms and predictive models that far surpass the understanding of the business domain expert that relies upon this technique to glean analytic insight.

But it doesn’t have to. According to Paxata VP of Marketing Michele Goetz—a former analyst for Forrester—an artful combination of machine learning, data visualizations, natural language processing, and a heaping of semantic technologies can render this technology functional to business analyst end users for enhanced analytic insight.

“There are analytics and visualizations that pull out of the machine learning that provide a bit of insight without having to go fully down the analytics path to get that type of insight,” Goetz commented. “Just having something that you’re continuously refining and tuning is interesting when you’re working in a much more exploratory fashion.”

Democratizing Machine Learning

Delivering machine learning into the hands of the business for well-governed, analytic profundity not predicated on lengthy time spent waiting on IT departments and data scientists is based on the capabilities of self-service platforms like Paxata, which specializes in data preparation. Such tools incorporate machine learning into broader capabilities associated with data preparation and transformation in order to streamline— and hasten—the steps leading to analytics or application-based action. Machine learning’s capacity to reconcile disparate sets of data in time frames commensurate with contemporary business practices may be the most convincing application of this technology’s ability to impact the work of a business analyst. This fact is exemplified by the granular degree of understanding machine learning provides in seemingly unconnected data sets in which it, “Really understands the data within those columns and understands the abstraction and the meaning of the data within there so that it can be suggestive in the way you bring those data sets together and reconcile that information,” Goetz remarked.

Data Exploration

Self-service machine learning capabilities are also an integral aspect of exploring data prior to analytics. By perceiving the results of how data interrelates based on precedents adhering to semantic notions of nomenclature, taxonomies, and ontologies, machine learning is able to give the end user results “in a way that isn’t just to say that it’s a confidence match, but also gets down more to the context and the meaning of that,” Goetz explained. In this respect, machine learning is a critical tool for investigating different sets of data, and actually expanding the sources and quantities of data that are leveraged for operations beyond those which are typically used. “It’s both a facet of investigation and reconciliation of the data process itself,” Goetz said.

Behavioral Action

Goetz also mentioned that by incorporating—and parsing through—the semantics of data, machine learning technologies are able to “provide a richer foundation” for linking and contextualizing data sets. The result is that these capabilities will enable users (who might not understand the intricacies of data classifications) to nonetheless leverage them with a more informed approach to both marrying data sets and comprehending their significance. By facilitating this utility in a self-service fashion targeted for individual business users, machine learning can play a substantial role in overcoming the influence of dark data, which Goetz described as the situation wherein “only about 12 percent of the data is truly under governance and managed to the highest quality because that’s what represents 80 percent of what operations works off of. But analytics has to go further. It’s diving into the 88 percent of the data that’s left over. There are no capabilities for that today except for data preparation.”

From Declarative to Inferred Learning

Much of the machine learning functionality available to the enterprise today (particularly in self-service platforms) is declarative in nature—meaning that precedents are established and declared, and serve as the basis for future actions. However, these capabilities are rapidly expanding to include what Goetz termed as “more of the behavioral aspects and the decisions that analytics users and data consumers are making as they’re determining, based off the suggestions, what they do with them and the data preparation processes, and linking the behavioral preparation steps of action further into the machine learning capabilities.” This expansion of machine learning is projected to also include more facets of domain knowledge, which should further provide business users with the tools to leverage this technology autonomously, yet within the governed capabilities of platforms based on consistent semantics.

“Coming in 2017, there will be much more of inferred machine learning techniques, which means you don’t have to introduce a declaration or a rule,” Goetz said. “That’s where the evolution is going. It [machine learning] will monitor the behavior in the background and incorporate that. It’s a bit more emergent in nature.”

Domain Specific Natural Language Processing

The potential for self-service machine learning to transition from a rule-based paradigm to an inference-based one is greatly enhanced by natural language processing and the underlying semantic technologies that fuel both of these capabilities. Furthermore, the enriched domain expertise of machine learning models is also based on the propensity for IT systems to understand natural language—as it applies to classifications, nomenclature, and standardized terms and requisite definitions. The advent of NLP in machine learning-sensitive environments assists with domain expertise in myriad verticals, particularly those that are heavily regulated. Goetz noted that this combination “is particularly useful when we’re talking about key industries like health care, financial services, and life sciences. Those are areas that have very specific languages that they have to adhere to.” Consequently, the standardized approach that natural language processing can bring to these regulated industries will serve business users when machine learning techniques are inferring various forms of data preparation or analytics. “When standard references are incorporated, it really provides a lot of power and speed when working with the data,” Goetz observed.

Business Autonomy

Virtually all of the trends impacting machine learning are facilitated in a self-service environment which extends the utility of the business in leveraging data-driven practices. Whether enabling these users to better understand their data for more informed and expedient integration, or simply improving the data discovery process, machine learning capabilities are increasing business autonomy. This proclivity is augmented by natural language processing and semantic technologies, which contribute to more instinctive understanding of business domains. When one factors in the evolution of machine learning from rule-based to inference-based action, it becomes clear that this technology could very well become an integral aspect of data-centric business practices across verticals.

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